what is reviews research

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• Literature Review: The What, Why and How-to Guide
• Introduction

Literature Review: The What, Why and How-to Guide — Introduction

• Getting Started
• How to Pick a Topic
• Strategies to Find Sources
• Evaluating Sources & Lit. Reviews
• Tips for Writing Literature Reviews
• Writing Literature Review: Useful Sites
• Citation Resources
• Other Academic Writings

What are Literature Reviews?

So, what is a literature review? "A literature review is an account of what has been published on a topic by accredited scholars and researchers. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available, or a set of summaries." Taylor, D.  The literature review: A few tips on conducting it . University of Toronto Health Sciences Writing Centre.

Goals of Literature Reviews

What are the goals of creating a Literature Review?  A literature could be written to accomplish different aims:

• To develop a theory or evaluate an existing theory
• To summarize the historical or existing state of a research topic
• Identify a problem in a field of research 

Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews .  Review of General Psychology , 1 (3), 311-320.

What kinds of sources require a Literature Review?

• A research paper assigned in a course
• A thesis or dissertation
• A grant proposal
• An article intended for publication in a journal

All these instances require you to collect what has been written about your research topic so that you can demonstrate how your own research sheds new light on the topic.

Types of Literature Reviews

What kinds of literature reviews are written?

Narrative review: The purpose of this type of review is to describe the current state of the research on a specific topic/research and to offer a critical analysis of the literature reviewed. Studies are grouped by research/theoretical categories, and themes and trends, strengths and weakness, and gaps are identified. The review ends with a conclusion section which summarizes the findings regarding the state of the research of the specific study, the gaps identify and if applicable, explains how the author's research will address gaps identify in the review and expand the knowledge on the topic reviewed.

• Example : Predictors and Outcomes of U.S. Quality Maternity Leave: A Review and Conceptual Framework:  10.1177/08948453211037398  

Systematic review : "The authors of a systematic review use a specific procedure to search the research literature, select the studies to include in their review, and critically evaluate the studies they find." (p. 139). Nelson, L. K. (2013). Research in Communication Sciences and Disorders . Plural Publishing.

• Example : The effect of leave policies on increasing fertility: a systematic review:  10.1057/s41599-022-01270-w

Meta-analysis : "Meta-analysis is a method of reviewing research findings in a quantitative fashion by transforming the data from individual studies into what is called an effect size and then pooling and analyzing this information. The basic goal in meta-analysis is to explain why different outcomes have occurred in different studies." (p. 197). Roberts, M. C., & Ilardi, S. S. (2003). Handbook of Research Methods in Clinical Psychology . Blackwell Publishing.

• Example : Employment Instability and Fertility in Europe: A Meta-Analysis:  10.1215/00703370-9164737

Meta-synthesis : "Qualitative meta-synthesis is a type of qualitative study that uses as data the findings from other qualitative studies linked by the same or related topic." (p.312). Zimmer, L. (2006). Qualitative meta-synthesis: A question of dialoguing with texts .  Journal of Advanced Nursing , 53 (3), 311-318.

• Example : Women’s perspectives on career successes and barriers: A qualitative meta-synthesis:  10.1177/05390184221113735

Literature Reviews in the Health Sciences

• UConn Health subject guide on systematic reviews Explanation of the different review types used in health sciences literature as well as tools to help you find the right review type
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Literature reviews, what is a literature review, learning more about how to do a literature review.

• Planning the Review
• The Research Question
• Choosing Where to Search
• Organizing the Review
• Writing the Review

A literature review is a review and synthesis of existing research on a topic or research question. A literature review is meant to analyze the scholarly literature, make connections across writings and identify strengths, weaknesses, trends, and missing conversations. A literature review should address different aspects of a topic as it relates to your research question. A literature review goes beyond a description or summary of the literature you have read. 

• Sage Research Methods Core Collection This link opens in a new window SAGE Research Methods supports research at all levels by providing material to guide users through every step of the research process. SAGE Research Methods is the ultimate methods library with more than 1000 books, reference works, journal articles, and instructional videos by world-leading academics from across the social sciences, including the largest collection of qualitative methods books available online from any scholarly publisher. – Publisher

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Literature Review: The What, Why and How-to Guide: Literature Reviews?

• Literature Reviews?
• Strategies to Finding Sources
• Keeping up with Research!
• Evaluating Sources & Literature Reviews
• Organizing for Writing
• Writing Literature Review
• Other Academic Writings

What is a Literature Review?

So, what is a literature review .

"A literature review is an account of what has been published on a topic by accredited scholars and researchers. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available or a set of summaries." - Quote from Taylor, D. (n.d)."The Literature Review: A Few Tips on Conducting it".

• Citation: "The Literature Review: A Few Tips on Conducting it"

What kinds of literature reviews are written?

Each field has a particular way to do reviews for academic research literature. In the social sciences and humanities the most common are:

• Narrative Reviews: The purpose of this type of review is to describe the current state of the research on a specific research topic and to offer a critical analysis of the literature reviewed. Studies are grouped by research/theoretical categories, and themes and trends, strengths and weaknesses, and gaps are identified. The review ends with a conclusion section that summarizes the findings regarding the state of the research of the specific study, the gaps identify and if applicable, explains how the author's research will address gaps identify in the review and expand the knowledge on the topic reviewed.
• Book review essays/ Historiographical review essays : A type of literature review typical in History and related fields, e.g., Latin American studies. For example, the Latin American Research Review explains that the purpose of this type of review is to “(1) to familiarize readers with the subject, approach, arguments, and conclusions found in a group of books whose common focus is a historical period; a country or region within Latin America; or a practice, development, or issue of interest to specialists and others; (2) to locate these books within current scholarship, critical methodologies, and approaches; and (3) to probe the relation of these new books to previous work on the subject, especially canonical texts. Unlike individual book reviews, the cluster reviews found in LARR seek to address the state of the field or discipline and not solely the works at issue.” - LARR

What are the Goals of Creating a Literature Review?

• To develop a theory or evaluate an existing theory
• To summarize the historical or existing state of a research topic
• Identify a problem in a field of research 
• Baumeister, R.F. & Leary, M.R. (1997). "Writing narrative literature reviews," Review of General Psychology , 1(3), 311-320.

When do you need to write a Literature Review?

• When writing a prospectus or a thesis/dissertation
• When writing a research paper
• When writing a grant proposal

In all these cases you need to dedicate a chapter in these works to showcase what has been written about your research topic and to point out how your own research will shed new light into a body of scholarship.

Where I can find examples of Literature Reviews?

Note:  In the humanities, even if they don't use the term "literature review", they may have a dedicated  chapter that reviewed the "critical bibliography" or they incorporated that review in the introduction or first chapter of the dissertation, book, or article.

• UCSB electronic theses and dissertations In partnership with the Graduate Division, the UC Santa Barbara Library is making available theses and dissertations produced by UCSB students. Currently included in ADRL are theses and dissertations that were originally filed electronically, starting in 2011. In future phases of ADRL, all theses and dissertations created by UCSB students may be digitized and made available.

Where to Find Standalone Literature Reviews

Literature reviews are also written as standalone articles as a way to survey a particular research topic in-depth. This type of literature review looks at a topic from a historical perspective to see how the understanding of the topic has changed over time. 

• Find e-Journals for Standalone Literature Reviews The best way to get familiar with and to learn how to write literature reviews is by reading them. You can use our Journal Search option to find journals that specialize in publishing literature reviews from major disciplines like anthropology, sociology, etc. Usually these titles are called, "Annual Review of [discipline name] OR [Discipline name] Review. This option works best if you know the title of the publication you are looking for. Below are some examples of these journals! more... less... Journal Search can be found by hovering over the link for Research on the library website.

Social Sciences

• Annual Review of Anthropology
• Annual Review of Political Science
• Annual Review of Sociology
• Ethnic Studies Review

Hard science and health sciences:

• Annual Review of Biomedical Data Science
• Annual Review of Materials Science
• Systematic Review From journal site: "The journal Systematic Reviews encompasses all aspects of the design, conduct, and reporting of systematic reviews" in the health sciences.
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• Last Updated: Mar 5, 2024 11:44 AM
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Introduction to Systematic Reviews

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• First Online: 20 July 2022
• pp 2159–2177
• Cite this reference work entry

• Tianjing Li 3 ,
• Ian J. Saldanha 4 &
• Karen A. Robinson 5  

229 Accesses

A systematic review identifies and synthesizes all relevant studies that fit prespecified criteria to answer a research question. Systematic review methods can be used to answer many types of research questions. The type of question most relevant to trialists is the effects of treatments and is thus the focus of this chapter. We discuss the motivation for and importance of performing systematic reviews and their relevance to trialists. We introduce the key steps in completing a systematic review, including framing the question, searching for and selecting studies, collecting data, assessing risk of bias in included studies, conducting a qualitative synthesis and a quantitative synthesis (i.e., meta-analysis), grading the certainty of evidence, and writing the systematic review report. We also describe how to identify systematic reviews and how to assess their methodological rigor. We discuss the challenges and criticisms of systematic reviews, and how technology and innovations, combined with a closer partnership between trialists and systematic reviewers, can help identify effective and safe evidence-based practices more quickly.

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What Is the Difference Between a Systematic Review and a Meta-analysis?

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Tianjing Li

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Ian J. Saldanha

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Li, T., Saldanha, I.J., Robinson, K.A. (2022). Introduction to Systematic Reviews. In: Piantadosi, S., Meinert, C.L. (eds) Principles and Practice of Clinical Trials. Springer, Cham. https://doi.org/10.1007/978-3-319-52636-2_194

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• Systematic Review | Definition, Examples & Guide

Systematic Review | Definition, Examples & Guide

Published on 15 June 2022 by Shaun Turney . Revised on 17 October 2022.

A systematic review is a type of review that uses repeatable methods to find, select, and synthesise all available evidence. It answers a clearly formulated research question and explicitly states the methods used to arrive at the answer.

They answered the question ‘What is the effectiveness of probiotics in reducing eczema symptoms and improving quality of life in patients with eczema?’

In this context, a probiotic is a health product that contains live microorganisms and is taken by mouth. Eczema is a common skin condition that causes red, itchy skin.

Table of contents

What is a systematic review, systematic review vs meta-analysis, systematic review vs literature review, systematic review vs scoping review, when to conduct a systematic review, pros and cons of systematic reviews, step-by-step example of a systematic review, frequently asked questions about systematic reviews.

A review is an overview of the research that’s already been completed on a topic.

What makes a systematic review different from other types of reviews is that the research methods are designed to reduce research bias . The methods are repeatable , and the approach is formal and systematic:

• Formulate a research question
• Develop a protocol
• Search for all relevant studies
• Apply the selection criteria
• Extract the data
• Synthesise the data
• Write and publish a report

Although multiple sets of guidelines exist, the Cochrane Handbook for Systematic Reviews is among the most widely used. It provides detailed guidelines on how to complete each step of the systematic review process.

Systematic reviews are most commonly used in medical and public health research, but they can also be found in other disciplines.

Systematic reviews typically answer their research question by synthesising all available evidence and evaluating the quality of the evidence. Synthesising means bringing together different information to tell a single, cohesive story. The synthesis can be narrative ( qualitative ), quantitative , or both.

Prevent plagiarism, run a free check.

Systematic reviews often quantitatively synthesise the evidence using a meta-analysis . A meta-analysis is a statistical analysis, not a type of review.

A meta-analysis is a technique to synthesise results from multiple studies. It’s a statistical analysis that combines the results of two or more studies, usually to estimate an effect size .

A literature review is a type of review that uses a less systematic and formal approach than a systematic review. Typically, an expert in a topic will qualitatively summarise and evaluate previous work, without using a formal, explicit method.

Although literature reviews are often less time-consuming and can be insightful or helpful, they have a higher risk of bias and are less transparent than systematic reviews.

Similar to a systematic review, a scoping review is a type of review that tries to minimise bias by using transparent and repeatable methods.

However, a scoping review isn’t a type of systematic review. The most important difference is the goal: rather than answering a specific question, a scoping review explores a topic. The researcher tries to identify the main concepts, theories, and evidence, as well as gaps in the current research.

Sometimes scoping reviews are an exploratory preparation step for a systematic review, and sometimes they are a standalone project.

A systematic review is a good choice of review if you want to answer a question about the effectiveness of an intervention , such as a medical treatment.

To conduct a systematic review, you’ll need the following:

• A precise question , usually about the effectiveness of an intervention. The question needs to be about a topic that’s previously been studied by multiple researchers. If there’s no previous research, there’s nothing to review.
• If you’re doing a systematic review on your own (e.g., for a research paper or thesis), you should take appropriate measures to ensure the validity and reliability of your research.
• Access to databases and journal archives. Often, your educational institution provides you with access.
• Time. A professional systematic review is a time-consuming process: it will take the lead author about six months of full-time work. If you’re a student, you should narrow the scope of your systematic review and stick to a tight schedule.
• Bibliographic, word-processing, spreadsheet, and statistical software . For example, you could use EndNote, Microsoft Word, Excel, and SPSS.

A systematic review has many pros .

• They minimise research b ias by considering all available evidence and evaluating each study for bias.
• Their methods are transparent , so they can be scrutinised by others.
• They’re thorough : they summarise all available evidence.
• They can be replicated and updated by others.

Systematic reviews also have a few cons .

• They’re time-consuming .
• They’re narrow in scope : they only answer the precise research question.

The 7 steps for conducting a systematic review are explained with an example.

Step 1: Formulate a research question

Formulating the research question is probably the most important step of a systematic review. A clear research question will:

• Allow you to more effectively communicate your research to other researchers and practitioners
• Guide your decisions as you plan and conduct your systematic review

A good research question for a systematic review has four components, which you can remember with the acronym PICO :

• Population(s) or problem(s)
• Intervention(s)
• Comparison(s)

You can rearrange these four components to write your research question:

• What is the effectiveness of I versus C for O in P ?

Sometimes, you may want to include a fourth component, the type of study design . In this case, the acronym is PICOT .

• Type of study design(s)
• The population of patients with eczema
• The intervention of probiotics
• In comparison to no treatment, placebo , or non-probiotic treatment
• The outcome of changes in participant-, parent-, and doctor-rated symptoms of eczema and quality of life
• Randomised control trials, a type of study design

Their research question was:

• What is the effectiveness of probiotics versus no treatment, a placebo, or a non-probiotic treatment for reducing eczema symptoms and improving quality of life in patients with eczema?

Step 2: Develop a protocol

A protocol is a document that contains your research plan for the systematic review. This is an important step because having a plan allows you to work more efficiently and reduces bias.

Your protocol should include the following components:

• Background information : Provide the context of the research question, including why it’s important.
• Research objective(s) : Rephrase your research question as an objective.
• Selection criteria: State how you’ll decide which studies to include or exclude from your review.
• Search strategy: Discuss your plan for finding studies.
• Analysis: Explain what information you’ll collect from the studies and how you’ll synthesise the data.

If you’re a professional seeking to publish your review, it’s a good idea to bring together an advisory committee . This is a group of about six people who have experience in the topic you’re researching. They can help you make decisions about your protocol.

It’s highly recommended to register your protocol. Registering your protocol means submitting it to a database such as PROSPERO or ClinicalTrials.gov .

Step 3: Search for all relevant studies

Searching for relevant studies is the most time-consuming step of a systematic review.

To reduce bias, it’s important to search for relevant studies very thoroughly. Your strategy will depend on your field and your research question, but sources generally fall into these four categories:

• Databases: Search multiple databases of peer-reviewed literature, such as PubMed or Scopus . Think carefully about how to phrase your search terms and include multiple synonyms of each word. Use Boolean operators if relevant.
• Handsearching: In addition to searching the primary sources using databases, you’ll also need to search manually. One strategy is to scan relevant journals or conference proceedings. Another strategy is to scan the reference lists of relevant studies.
• Grey literature: Grey literature includes documents produced by governments, universities, and other institutions that aren’t published by traditional publishers. Graduate student theses are an important type of grey literature, which you can search using the Networked Digital Library of Theses and Dissertations (NDLTD) . In medicine, clinical trial registries are another important type of grey literature.
• Experts: Contact experts in the field to ask if they have unpublished studies that should be included in your review.

At this stage of your review, you won’t read the articles yet. Simply save any potentially relevant citations using bibliographic software, such as Scribbr’s APA or MLA Generator .

• Databases: EMBASE, PsycINFO, AMED, LILACS, and ISI Web of Science
• Handsearch: Conference proceedings and reference lists of articles
• Grey literature: The Cochrane Library, the metaRegister of Controlled Trials, and the Ongoing Skin Trials Register
• Experts: Authors of unpublished registered trials, pharmaceutical companies, and manufacturers of probiotics

Step 4: Apply the selection criteria

Applying the selection criteria is a three-person job. Two of you will independently read the studies and decide which to include in your review based on the selection criteria you established in your protocol . The third person’s job is to break any ties.

To increase inter-rater reliability , ensure that everyone thoroughly understands the selection criteria before you begin.

If you’re writing a systematic review as a student for an assignment, you might not have a team. In this case, you’ll have to apply the selection criteria on your own; you can mention this as a limitation in your paper’s discussion.

You should apply the selection criteria in two phases:

• Based on the titles and abstracts : Decide whether each article potentially meets the selection criteria based on the information provided in the abstracts.
• Based on the full texts: Download the articles that weren’t excluded during the first phase. If an article isn’t available online or through your library, you may need to contact the authors to ask for a copy. Read the articles and decide which articles meet the selection criteria.

It’s very important to keep a meticulous record of why you included or excluded each article. When the selection process is complete, you can summarise what you did using a PRISMA flow diagram .

Next, Boyle and colleagues found the full texts for each of the remaining studies. Boyle and Tang read through the articles to decide if any more studies needed to be excluded based on the selection criteria.

When Boyle and Tang disagreed about whether a study should be excluded, they discussed it with Varigos until the three researchers came to an agreement.

Step 5: Extract the data

Extracting the data means collecting information from the selected studies in a systematic way. There are two types of information you need to collect from each study:

• Information about the study’s methods and results . The exact information will depend on your research question, but it might include the year, study design , sample size, context, research findings , and conclusions. If any data are missing, you’ll need to contact the study’s authors.
• Your judgement of the quality of the evidence, including risk of bias .

You should collect this information using forms. You can find sample forms in The Registry of Methods and Tools for Evidence-Informed Decision Making and the Grading of Recommendations, Assessment, Development and Evaluations Working Group .

Extracting the data is also a three-person job. Two people should do this step independently, and the third person will resolve any disagreements.

They also collected data about possible sources of bias, such as how the study participants were randomised into the control and treatment groups.

Step 6: Synthesise the data

Synthesising the data means bringing together the information you collected into a single, cohesive story. There are two main approaches to synthesising the data:

• Narrative ( qualitative ): Summarise the information in words. You’ll need to discuss the studies and assess their overall quality.
• Quantitative : Use statistical methods to summarise and compare data from different studies. The most common quantitative approach is a meta-analysis , which allows you to combine results from multiple studies into a summary result.

Generally, you should use both approaches together whenever possible. If you don’t have enough data, or the data from different studies aren’t comparable, then you can take just a narrative approach. However, you should justify why a quantitative approach wasn’t possible.

Boyle and colleagues also divided the studies into subgroups, such as studies about babies, children, and adults, and analysed the effect sizes within each group.

Step 7: Write and publish a report

The purpose of writing a systematic review article is to share the answer to your research question and explain how you arrived at this answer.

Your article should include the following sections:

• Abstract : A summary of the review
• Introduction : Including the rationale and objectives
• Methods : Including the selection criteria, search method, data extraction method, and synthesis method
• Results : Including results of the search and selection process, study characteristics, risk of bias in the studies, and synthesis results
• Discussion : Including interpretation of the results and limitations of the review
• Conclusion : The answer to your research question and implications for practice, policy, or research

To verify that your report includes everything it needs, you can use the PRISMA checklist .

Once your report is written, you can publish it in a systematic review database, such as the Cochrane Database of Systematic Reviews , and/or in a peer-reviewed journal.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a dissertation , thesis, research paper , or proposal .

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarise yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Turney, S. (2022, October 17). Systematic Review | Definition, Examples & Guide. Scribbr. Retrieved 27 May 2024, from https://www.scribbr.co.uk/research-methods/systematic-reviews/

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• 04 December 2020
• Correction 09 December 2020

How to write a superb literature review

Andy Tay is a freelance writer based in Singapore.

You can also search for this author in PubMed   Google Scholar

Literature reviews are important resources for scientists. They provide historical context for a field while offering opinions on its future trajectory. Creating them can provide inspiration for one’s own research, as well as some practice in writing. But few scientists are trained in how to write a review — or in what constitutes an excellent one. Even picking the appropriate software to use can be an involved decision (see ‘Tools and techniques’). So Nature asked editors and working scientists with well-cited reviews for their tips.

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doi: https://doi.org/10.1038/d41586-020-03422-x

Interviews have been edited for length and clarity.

Updates & Corrections

Correction 09 December 2020 : An earlier version of the tables in this article included some incorrect details about the programs Zotero, Endnote and Manubot. These have now been corrected.

Hsing, I.-M., Xu, Y. & Zhao, W. Electroanalysis 19 , 755–768 (2007).

Article   Google Scholar  

Ledesma, H. A. et al. Nature Nanotechnol. 14 , 645–657 (2019).

Article   PubMed   Google Scholar  

Brahlek, M., Koirala, N., Bansal, N. & Oh, S. Solid State Commun. 215–216 , 54–62 (2015).

Choi, Y. & Lee, S. Y. Nature Rev. Chem . https://doi.org/10.1038/s41570-020-00221-w (2020).

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Literature Reviews

• What is a literature review?
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• Define your research question
• Determine inclusion and exclusion criteria
• Choose databases and search
• Review Results
• Synthesize Results
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What is a Literature Review?

A literature or narrative review is a comprehensive review and analysis of the published literature on a specific topic or research question. The literature that is reviewed contains: books, articles, academic articles, conference proceedings, association papers, and dissertations. It contains the most pertinent studies and points to important past and current research and practices. It provides background and context, and shows how your research will contribute to the field. 

A literature review should: 

• Provide a comprehensive and updated review of the literature;
• Explain why this review has taken place;
• Articulate a position or hypothesis;
• Acknowledge and account for conflicting and corroborating points of view

From  S age Research Methods

Purpose of a Literature Review

A literature review can be written as an introduction to a study to:

• Demonstrate how a study fills a gap in research
• Compare a study with other research that's been done

Or it can be a separate work (a research article on its own) which:

• Organizes or describes a topic
• Describes variables within a particular issue/problem

Limitations of a Literature Review

Some of the limitations of a literature review are:

• It's a snapshot in time. Unlike other reviews, this one has beginning, a middle and an end. There may be future developments that could make your work less relevant.
• It may be too focused. Some niche studies may miss the bigger picture.
• It can be difficult to be comprehensive. There is no way to make sure all the literature on a topic was considered.
• It is easy to be biased if you stick to top tier journals. There may be other places where people are publishing exemplary research. Look to open access publications and conferences to reflect a more inclusive collection. Also, make sure to include opposing views (and not just supporting evidence).

Source: Grant, Maria J., and Andrew Booth. “A Typology of Reviews: An Analysis of 14 Review Types and Associated Methodologies.” Health Information & Libraries Journal, vol. 26, no. 2, June 2009, pp. 91–108. Wiley Online Library, doi:10.1111/j.1471-1842.2009.00848.x.

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For help in other subject areas, please see the guide to library specialists by subject .

Periodically, UT Libraries runs a workshop covering the basics and library support for literature reviews. While we try to offer these once per academic year, we find providing the recording to be helpful to community members who have missed the session. Following is the most recent recording of the workshop, Conducting a Literature Review. To view the recording, a UT login is required.

• October 26, 2022 recording
• Last Updated: Oct 26, 2022 2:49 PM
• URL: https://guides.lib.utexas.edu/literaturereviews

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What is a Literature Review? How to Write It (with Examples)

A literature review is a critical analysis and synthesis of existing research on a particular topic. It provides an overview of the current state of knowledge, identifies gaps, and highlights key findings in the literature. 1 The purpose of a literature review is to situate your own research within the context of existing scholarship, demonstrating your understanding of the topic and showing how your work contributes to the ongoing conversation in the field. Learning how to write a literature review is a critical tool for successful research. Your ability to summarize and synthesize prior research pertaining to a certain topic demonstrates your grasp on the topic of study, and assists in the learning process. 

Table of Contents

• What is the purpose of literature review? 
• a. Habitat Loss and Species Extinction: 
• b. Range Shifts and Phenological Changes: 
• c. Ocean Acidification and Coral Reefs: 
• d. Adaptive Strategies and Conservation Efforts: 

How to write a good literature review 

• Choose a Topic and Define the Research Question: 
• Decide on the Scope of Your Review: 
• Select Databases for Searches: 
• Conduct Searches and Keep Track: 
• Review the Literature: 
• Organize and Write Your Literature Review: 
• How to write a literature review faster with Paperpal? 
• Frequently asked questions 

What is a literature review?

A well-conducted literature review demonstrates the researcher’s familiarity with the existing literature, establishes the context for their own research, and contributes to scholarly conversations on the topic. One of the purposes of a literature review is also to help researchers avoid duplicating previous work and ensure that their research is informed by and builds upon the existing body of knowledge.

What is the purpose of literature review?

A literature review serves several important purposes within academic and research contexts. Here are some key objectives and functions of a literature review: 2  

1. Contextualizing the Research Problem: The literature review provides a background and context for the research problem under investigation. It helps to situate the study within the existing body of knowledge. 

2. Identifying Gaps in Knowledge: By identifying gaps, contradictions, or areas requiring further research, the researcher can shape the research question and justify the significance of the study. This is crucial for ensuring that the new research contributes something novel to the field. 

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3. Understanding Theoretical and Conceptual Frameworks: Literature reviews help researchers gain an understanding of the theoretical and conceptual frameworks used in previous studies. This aids in the development of a theoretical framework for the current research. 

4. Providing Methodological Insights: Another purpose of literature reviews is that it allows researchers to learn about the methodologies employed in previous studies. This can help in choosing appropriate research methods for the current study and avoiding pitfalls that others may have encountered. 

5. Establishing Credibility: A well-conducted literature review demonstrates the researcher’s familiarity with existing scholarship, establishing their credibility and expertise in the field. It also helps in building a solid foundation for the new research. 

6. Informing Hypotheses or Research Questions: The literature review guides the formulation of hypotheses or research questions by highlighting relevant findings and areas of uncertainty in existing literature. 

Literature review example

Let’s delve deeper with a literature review example: Let’s say your literature review is about the impact of climate change on biodiversity. You might format your literature review into sections such as the effects of climate change on habitat loss and species extinction, phenological changes, and marine biodiversity. Each section would then summarize and analyze relevant studies in those areas, highlighting key findings and identifying gaps in the research. The review would conclude by emphasizing the need for further research on specific aspects of the relationship between climate change and biodiversity. The following literature review template provides a glimpse into the recommended literature review structure and content, demonstrating how research findings are organized around specific themes within a broader topic. 

Literature Review on Climate Change Impacts on Biodiversity:

Climate change is a global phenomenon with far-reaching consequences, including significant impacts on biodiversity. This literature review synthesizes key findings from various studies: 

a. Habitat Loss and Species Extinction:

Climate change-induced alterations in temperature and precipitation patterns contribute to habitat loss, affecting numerous species (Thomas et al., 2004). The review discusses how these changes increase the risk of extinction, particularly for species with specific habitat requirements. 

b. Range Shifts and Phenological Changes:

Observations of range shifts and changes in the timing of biological events (phenology) are documented in response to changing climatic conditions (Parmesan & Yohe, 2003). These shifts affect ecosystems and may lead to mismatches between species and their resources. 

c. Ocean Acidification and Coral Reefs:

The review explores the impact of climate change on marine biodiversity, emphasizing ocean acidification’s threat to coral reefs (Hoegh-Guldberg et al., 2007). Changes in pH levels negatively affect coral calcification, disrupting the delicate balance of marine ecosystems. 

d. Adaptive Strategies and Conservation Efforts:

Recognizing the urgency of the situation, the literature review discusses various adaptive strategies adopted by species and conservation efforts aimed at mitigating the impacts of climate change on biodiversity (Hannah et al., 2007). It emphasizes the importance of interdisciplinary approaches for effective conservation planning. 

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Writing a literature review involves summarizing and synthesizing existing research on a particular topic. A good literature review format should include the following elements. 

Introduction: The introduction sets the stage for your literature review, providing context and introducing the main focus of your review. 

• Opening Statement: Begin with a general statement about the broader topic and its significance in the field. 
• Scope and Purpose: Clearly define the scope of your literature review. Explain the specific research question or objective you aim to address. 
• Organizational Framework: Briefly outline the structure of your literature review, indicating how you will categorize and discuss the existing research. 
• Significance of the Study: Highlight why your literature review is important and how it contributes to the understanding of the chosen topic. 
• Thesis Statement: Conclude the introduction with a concise thesis statement that outlines the main argument or perspective you will develop in the body of the literature review. 

Body: The body of the literature review is where you provide a comprehensive analysis of existing literature, grouping studies based on themes, methodologies, or other relevant criteria. 

• Organize by Theme or Concept: Group studies that share common themes, concepts, or methodologies. Discuss each theme or concept in detail, summarizing key findings and identifying gaps or areas of disagreement. 
• Critical Analysis: Evaluate the strengths and weaknesses of each study. Discuss the methodologies used, the quality of evidence, and the overall contribution of each work to the understanding of the topic. 
• Synthesis of Findings: Synthesize the information from different studies to highlight trends, patterns, or areas of consensus in the literature. 
• Identification of Gaps: Discuss any gaps or limitations in the existing research and explain how your review contributes to filling these gaps. 
• Transition between Sections: Provide smooth transitions between different themes or concepts to maintain the flow of your literature review. 

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Conclusion: The conclusion of your literature review should summarize the main findings, highlight the contributions of the review, and suggest avenues for future research. 

• Summary of Key Findings: Recap the main findings from the literature and restate how they contribute to your research question or objective. 
• Contributions to the Field: Discuss the overall contribution of your literature review to the existing knowledge in the field. 
• Implications and Applications: Explore the practical implications of the findings and suggest how they might impact future research or practice. 
• Recommendations for Future Research: Identify areas that require further investigation and propose potential directions for future research in the field. 
• Final Thoughts: Conclude with a final reflection on the importance of your literature review and its relevance to the broader academic community. 

Conducting a literature review

Conducting a literature review is an essential step in research that involves reviewing and analyzing existing literature on a specific topic. It’s important to know how to do a literature review effectively, so here are the steps to follow: 1  

Choose a Topic and Define the Research Question:

• Select a topic that is relevant to your field of study. 
• Clearly define your research question or objective. Determine what specific aspect of the topic do you want to explore? 

Decide on the Scope of Your Review:

• Determine the timeframe for your literature review. Are you focusing on recent developments, or do you want a historical overview? 
• Consider the geographical scope. Is your review global, or are you focusing on a specific region? 
• Define the inclusion and exclusion criteria. What types of sources will you include? Are there specific types of studies or publications you will exclude? 

Select Databases for Searches:

• Identify relevant databases for your field. Examples include PubMed, IEEE Xplore, Scopus, Web of Science, and Google Scholar. 
• Consider searching in library catalogs, institutional repositories, and specialized databases related to your topic. 

Conduct Searches and Keep Track:

• Develop a systematic search strategy using keywords, Boolean operators (AND, OR, NOT), and other search techniques. 
• Record and document your search strategy for transparency and replicability. 
• Keep track of the articles, including publication details, abstracts, and links. Use citation management tools like EndNote, Zotero, or Mendeley to organize your references. 

Review the Literature:

• Evaluate the relevance and quality of each source. Consider the methodology, sample size, and results of studies. 
• Organize the literature by themes or key concepts. Identify patterns, trends, and gaps in the existing research. 
• Summarize key findings and arguments from each source. Compare and contrast different perspectives. 
• Identify areas where there is a consensus in the literature and where there are conflicting opinions. 
• Provide critical analysis and synthesis of the literature. What are the strengths and weaknesses of existing research? 

Organize and Write Your Literature Review:

• Literature review outline should be based on themes, chronological order, or methodological approaches. 
• Write a clear and coherent narrative that synthesizes the information gathered. 
• Use proper citations for each source and ensure consistency in your citation style (APA, MLA, Chicago, etc.). 
• Conclude your literature review by summarizing key findings, identifying gaps, and suggesting areas for future research. 

Whether you’re exploring a new research field or finding new angles to develop an existing topic, sifting through hundreds of papers can take more time than you have to spare. But what if you could find science-backed insights with verified citations in seconds? That’s the power of Paperpal’s new Research feature!  

How to write a literature review faster with Paperpal?

Paperpal, an AI writing assistant, integrates powerful academic search capabilities within its writing platform. With the Research feature, you get 100% factual insights, with citations backed by 250M+ verified research articles, directly within your writing interface with the option to save relevant references in your Citation Library. By eliminating the need to switch tabs to find answers to all your research questions, Paperpal saves time and helps you stay focused on your writing.   

Here’s how to use the Research feature:  

• Ask a question: Get started with a new document on paperpal.com. Click on the “Research” feature and type your question in plain English. Paperpal will scour over 250 million research articles, including conference papers and preprints, to provide you with accurate insights and citations. 
• Review and Save: Paperpal summarizes the information, while citing sources and listing relevant reads. You can quickly scan the results to identify relevant references and save these directly to your built-in citations library for later access. 
• Cite with Confidence: Paperpal makes it easy to incorporate relevant citations and references into your writing, ensuring your arguments are well-supported by credible sources. This translates to a polished, well-researched literature review. 

The literature review sample and detailed advice on writing and conducting a review will help you produce a well-structured report. But remember that a good literature review is an ongoing process, and it may be necessary to revisit and update it as your research progresses. By combining effortless research with an easy citation process, Paperpal Research streamlines the literature review process and empowers you to write faster and with more confidence. Try Paperpal Research now and see for yourself.  

Frequently asked questions

A literature review is a critical and comprehensive analysis of existing literature (published and unpublished works) on a specific topic or research question and provides a synthesis of the current state of knowledge in a particular field. A well-conducted literature review is crucial for researchers to build upon existing knowledge, avoid duplication of efforts, and contribute to the advancement of their field. It also helps researchers situate their work within a broader context and facilitates the development of a sound theoretical and conceptual framework for their studies.

Literature review is a crucial component of research writing, providing a solid background for a research paper’s investigation. The aim is to keep professionals up to date by providing an understanding of ongoing developments within a specific field, including research methods, and experimental techniques used in that field, and present that knowledge in the form of a written report. Also, the depth and breadth of the literature review emphasizes the credibility of the scholar in his or her field.  

Before writing a literature review, it’s essential to undertake several preparatory steps to ensure that your review is well-researched, organized, and focused. This includes choosing a topic of general interest to you and doing exploratory research on that topic, writing an annotated bibliography, and noting major points, especially those that relate to the position you have taken on the topic. 

Literature reviews and academic research papers are essential components of scholarly work but serve different purposes within the academic realm. 3 A literature review aims to provide a foundation for understanding the current state of research on a particular topic, identify gaps or controversies, and lay the groundwork for future research. Therefore, it draws heavily from existing academic sources, including books, journal articles, and other scholarly publications. In contrast, an academic research paper aims to present new knowledge, contribute to the academic discourse, and advance the understanding of a specific research question. Therefore, it involves a mix of existing literature (in the introduction and literature review sections) and original data or findings obtained through research methods. 

Literature reviews are essential components of academic and research papers, and various strategies can be employed to conduct them effectively. If you want to know how to write a literature review for a research paper, here are four common approaches that are often used by researchers.  Chronological Review: This strategy involves organizing the literature based on the chronological order of publication. It helps to trace the development of a topic over time, showing how ideas, theories, and research have evolved.  Thematic Review: Thematic reviews focus on identifying and analyzing themes or topics that cut across different studies. Instead of organizing the literature chronologically, it is grouped by key themes or concepts, allowing for a comprehensive exploration of various aspects of the topic.  Methodological Review: This strategy involves organizing the literature based on the research methods employed in different studies. It helps to highlight the strengths and weaknesses of various methodologies and allows the reader to evaluate the reliability and validity of the research findings.  Theoretical Review: A theoretical review examines the literature based on the theoretical frameworks used in different studies. This approach helps to identify the key theories that have been applied to the topic and assess their contributions to the understanding of the subject.  It’s important to note that these strategies are not mutually exclusive, and a literature review may combine elements of more than one approach. The choice of strategy depends on the research question, the nature of the literature available, and the goals of the review. Additionally, other strategies, such as integrative reviews or systematic reviews, may be employed depending on the specific requirements of the research.

The literature review format can vary depending on the specific publication guidelines. However, there are some common elements and structures that are often followed. Here is a general guideline for the format of a literature review:  Introduction:   Provide an overview of the topic.  Define the scope and purpose of the literature review.  State the research question or objective.  Body:   Organize the literature by themes, concepts, or chronology.  Critically analyze and evaluate each source.  Discuss the strengths and weaknesses of the studies.  Highlight any methodological limitations or biases.  Identify patterns, connections, or contradictions in the existing research.  Conclusion:   Summarize the key points discussed in the literature review.  Highlight the research gap.  Address the research question or objective stated in the introduction.  Highlight the contributions of the review and suggest directions for future research.

Both annotated bibliographies and literature reviews involve the examination of scholarly sources. While annotated bibliographies focus on individual sources with brief annotations, literature reviews provide a more in-depth, integrated, and comprehensive analysis of existing literature on a specific topic. The key differences are as follows: 

References 

• Denney, A. S., & Tewksbury, R. (2013). How to write a literature review.  Journal of criminal justice education ,  24 (2), 218-234. 
• Pan, M. L. (2016).  Preparing literature reviews: Qualitative and quantitative approaches . Taylor & Francis. 
• Cantero, C. (2019). How to write a literature review.  San José State University Writing Center . 

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Systematic Reviews

• What is a Systematic Review?

A systematic review is an evidence synthesis that uses explicit, reproducible methods to perform a comprehensive literature search and critical appraisal of individual studies and that uses appropriate statistical techniques to combine these valid studies.

Key Characteristics of a Systematic Review:

Generally, systematic reviews must have:

• a clearly stated set of objectives with pre-defined eligibility criteria for studies
• an explicit, reproducible methodology
• a systematic search that attempts to identify all studies that would meet the eligibility criteria
• an assessment of the validity of the findings of the included studies, for example through the assessment of the risk of bias
• a systematic presentation, and synthesis, of the characteristics and findings of the included studies.

A meta-analysis is a systematic review that uses quantitative methods to synthesize and summarize the pooled data from included studies.

Additional Information

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• Systematic Reviews: CRD’s Guidance for Undertaking Reviews in Health Care From The University of York Centre for Reviews and Dissemination: Provides practical guidance for undertaking evidence synthesis based on a thorough understanding of systematic review methodology. It presents the core principles of systematic reviewing, and in complementary chapters, highlights issues that are specific to reviews of clinical tests, public health interventions, adverse effects, and economic evaluations.
• Cornell, Sytematic Reviews and Evidence Synthesis Beyond the Health Sciences Video series geared for librarians but very informative about searching outside medicine.
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Introduction to systematic review and meta-analysis

1 Department of Anesthesiology and Pain Medicine, Inje University Seoul Paik Hospital, Seoul, Korea

2 Department of Anesthesiology and Pain Medicine, Chung-Ang University College of Medicine, Seoul, Korea

Systematic reviews and meta-analyses present results by combining and analyzing data from different studies conducted on similar research topics. In recent years, systematic reviews and meta-analyses have been actively performed in various fields including anesthesiology. These research methods are powerful tools that can overcome the difficulties in performing large-scale randomized controlled trials. However, the inclusion of studies with any biases or improperly assessed quality of evidence in systematic reviews and meta-analyses could yield misleading results. Therefore, various guidelines have been suggested for conducting systematic reviews and meta-analyses to help standardize them and improve their quality. Nonetheless, accepting the conclusions of many studies without understanding the meta-analysis can be dangerous. Therefore, this article provides an easy introduction to clinicians on performing and understanding meta-analyses.

Introduction

A systematic review collects all possible studies related to a given topic and design, and reviews and analyzes their results [ 1 ]. During the systematic review process, the quality of studies is evaluated, and a statistical meta-analysis of the study results is conducted on the basis of their quality. A meta-analysis is a valid, objective, and scientific method of analyzing and combining different results. Usually, in order to obtain more reliable results, a meta-analysis is mainly conducted on randomized controlled trials (RCTs), which have a high level of evidence [ 2 ] ( Fig. 1 ). Since 1999, various papers have presented guidelines for reporting meta-analyses of RCTs. Following the Quality of Reporting of Meta-analyses (QUORUM) statement [ 3 ], and the appearance of registers such as Cochrane Library’s Methodology Register, a large number of systematic literature reviews have been registered. In 2009, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 4 ] was published, and it greatly helped standardize and improve the quality of systematic reviews and meta-analyses [ 5 ].

Levels of evidence.

In anesthesiology, the importance of systematic reviews and meta-analyses has been highlighted, and they provide diagnostic and therapeutic value to various areas, including not only perioperative management but also intensive care and outpatient anesthesia [6–13]. Systematic reviews and meta-analyses include various topics, such as comparing various treatments of postoperative nausea and vomiting [ 14 , 15 ], comparing general anesthesia and regional anesthesia [ 16 – 18 ], comparing airway maintenance devices [ 8 , 19 ], comparing various methods of postoperative pain control (e.g., patient-controlled analgesia pumps, nerve block, or analgesics) [ 20 – 23 ], comparing the precision of various monitoring instruments [ 7 ], and meta-analysis of dose-response in various drugs [ 12 ].

Thus, literature reviews and meta-analyses are being conducted in diverse medical fields, and the aim of highlighting their importance is to help better extract accurate, good quality data from the flood of data being produced. However, a lack of understanding about systematic reviews and meta-analyses can lead to incorrect outcomes being derived from the review and analysis processes. If readers indiscriminately accept the results of the many meta-analyses that are published, incorrect data may be obtained. Therefore, in this review, we aim to describe the contents and methods used in systematic reviews and meta-analyses in a way that is easy to understand for future authors and readers of systematic review and meta-analysis.

Study Planning

It is easy to confuse systematic reviews and meta-analyses. A systematic review is an objective, reproducible method to find answers to a certain research question, by collecting all available studies related to that question and reviewing and analyzing their results. A meta-analysis differs from a systematic review in that it uses statistical methods on estimates from two or more different studies to form a pooled estimate [ 1 ]. Following a systematic review, if it is not possible to form a pooled estimate, it can be published as is without progressing to a meta-analysis; however, if it is possible to form a pooled estimate from the extracted data, a meta-analysis can be attempted. Systematic reviews and meta-analyses usually proceed according to the flowchart presented in Fig. 2 . We explain each of the stages below.

Flowchart illustrating a systematic review.

Formulating research questions

A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies. Here, the definition of the word “similar” is not made clear, but when selecting a topic for the meta-analysis, it is essential to ensure that the different studies present data that can be combined. If the studies contain data on the same topic that can be combined, a meta-analysis can even be performed using data from only two studies. However, study selection via a systematic review is a precondition for performing a meta-analysis, and it is important to clearly define the Population, Intervention, Comparison, Outcomes (PICO) parameters that are central to evidence-based research. In addition, selection of the research topic is based on logical evidence, and it is important to select a topic that is familiar to readers without clearly confirmed the evidence [ 24 ].

Protocols and registration

In systematic reviews, prior registration of a detailed research plan is very important. In order to make the research process transparent, primary/secondary outcomes and methods are set in advance, and in the event of changes to the method, other researchers and readers are informed when, how, and why. Many studies are registered with an organization like PROSPERO ( http://www.crd.york.ac.uk/PROSPERO/ ), and the registration number is recorded when reporting the study, in order to share the protocol at the time of planning.

Defining inclusion and exclusion criteria

Information is included on the study design, patient characteristics, publication status (published or unpublished), language used, and research period. If there is a discrepancy between the number of patients included in the study and the number of patients included in the analysis, this needs to be clearly explained while describing the patient characteristics, to avoid confusing the reader.

Literature search and study selection

In order to secure proper basis for evidence-based research, it is essential to perform a broad search that includes as many studies as possible that meet the inclusion and exclusion criteria. Typically, the three bibliographic databases Medline, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) are used. In domestic studies, the Korean databases KoreaMed, KMBASE, and RISS4U may be included. Effort is required to identify not only published studies but also abstracts, ongoing studies, and studies awaiting publication. Among the studies retrieved in the search, the researchers remove duplicate studies, select studies that meet the inclusion/exclusion criteria based on the abstracts, and then make the final selection of studies based on their full text. In order to maintain transparency and objectivity throughout this process, study selection is conducted independently by at least two investigators. When there is a inconsistency in opinions, intervention is required via debate or by a third reviewer. The methods for this process also need to be planned in advance. It is essential to ensure the reproducibility of the literature selection process [ 25 ].

Quality of evidence

However, well planned the systematic review or meta-analysis is, if the quality of evidence in the studies is low, the quality of the meta-analysis decreases and incorrect results can be obtained [ 26 ]. Even when using randomized studies with a high quality of evidence, evaluating the quality of evidence precisely helps determine the strength of recommendations in the meta-analysis. One method of evaluating the quality of evidence in non-randomized studies is the Newcastle-Ottawa Scale, provided by the Ottawa Hospital Research Institute 1) . However, we are mostly focusing on meta-analyses that use randomized studies.

If the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system ( http://www.gradeworkinggroup.org/ ) is used, the quality of evidence is evaluated on the basis of the study limitations, inaccuracies, incompleteness of outcome data, indirectness of evidence, and risk of publication bias, and this is used to determine the strength of recommendations [ 27 ]. As shown in Table 1 , the study limitations are evaluated using the “risk of bias” method proposed by Cochrane 2) . This method classifies bias in randomized studies as “low,” “high,” or “unclear” on the basis of the presence or absence of six processes (random sequence generation, allocation concealment, blinding participants or investigators, incomplete outcome data, selective reporting, and other biases) [ 28 ].

The Cochrane Collaboration’s Tool for Assessing the Risk of Bias [ 28 ]

Data extraction

Two different investigators extract data based on the objectives and form of the study; thereafter, the extracted data are reviewed. Since the size and format of each variable are different, the size and format of the outcomes are also different, and slight changes may be required when combining the data [ 29 ]. If there are differences in the size and format of the outcome variables that cause difficulties combining the data, such as the use of different evaluation instruments or different evaluation timepoints, the analysis may be limited to a systematic review. The investigators resolve differences of opinion by debate, and if they fail to reach a consensus, a third-reviewer is consulted.

Data Analysis

The aim of a meta-analysis is to derive a conclusion with increased power and accuracy than what could not be able to achieve in individual studies. Therefore, before analysis, it is crucial to evaluate the direction of effect, size of effect, homogeneity of effects among studies, and strength of evidence [ 30 ]. Thereafter, the data are reviewed qualitatively and quantitatively. If it is determined that the different research outcomes cannot be combined, all the results and characteristics of the individual studies are displayed in a table or in a descriptive form; this is referred to as a qualitative review. A meta-analysis is a quantitative review, in which the clinical effectiveness is evaluated by calculating the weighted pooled estimate for the interventions in at least two separate studies.

The pooled estimate is the outcome of the meta-analysis, and is typically explained using a forest plot ( Figs. 3 and ​ and4). 4 ). The black squares in the forest plot are the odds ratios (ORs) and 95% confidence intervals in each study. The area of the squares represents the weight reflected in the meta-analysis. The black diamond represents the OR and 95% confidence interval calculated across all the included studies. The bold vertical line represents a lack of therapeutic effect (OR = 1); if the confidence interval includes OR = 1, it means no significant difference was found between the treatment and control groups.

Forest plot analyzed by two different models using the same data. (A) Fixed-effect model. (B) Random-effect model. The figure depicts individual trials as filled squares with the relative sample size and the solid line as the 95% confidence interval of the difference. The diamond shape indicates the pooled estimate and uncertainty for the combined effect. The vertical line indicates the treatment group shows no effect (OR = 1). Moreover, if the confidence interval includes 1, then the result shows no evidence of difference between the treatment and control groups.

Forest plot representing homogeneous data.

Dichotomous variables and continuous variables

In data analysis, outcome variables can be considered broadly in terms of dichotomous variables and continuous variables. When combining data from continuous variables, the mean difference (MD) and standardized mean difference (SMD) are used ( Table 2 ).

Summary of Meta-analysis Methods Available in RevMan [ 28 ]

The MD is the absolute difference in mean values between the groups, and the SMD is the mean difference between groups divided by the standard deviation. When results are presented in the same units, the MD can be used, but when results are presented in different units, the SMD should be used. When the MD is used, the combined units must be shown. A value of “0” for the MD or SMD indicates that the effects of the new treatment method and the existing treatment method are the same. A value lower than “0” means the new treatment method is less effective than the existing method, and a value greater than “0” means the new treatment is more effective than the existing method.

When combining data for dichotomous variables, the OR, risk ratio (RR), or risk difference (RD) can be used. The RR and RD can be used for RCTs, quasi-experimental studies, or cohort studies, and the OR can be used for other case-control studies or cross-sectional studies. However, because the OR is difficult to interpret, using the RR and RD, if possible, is recommended. If the outcome variable is a dichotomous variable, it can be presented as the number needed to treat (NNT), which is the minimum number of patients who need to be treated in the intervention group, compared to the control group, for a given event to occur in at least one patient. Based on Table 3 , in an RCT, if x is the probability of the event occurring in the control group and y is the probability of the event occurring in the intervention group, then x = c/(c + d), y = a/(a + b), and the absolute risk reduction (ARR) = x − y. NNT can be obtained as the reciprocal, 1/ARR.

Calculation of the Number Needed to Treat in the Dichotomous table

Fixed-effect models and random-effect models

In order to analyze effect size, two types of models can be used: a fixed-effect model or a random-effect model. A fixed-effect model assumes that the effect of treatment is the same, and that variation between results in different studies is due to random error. Thus, a fixed-effect model can be used when the studies are considered to have the same design and methodology, or when the variability in results within a study is small, and the variance is thought to be due to random error. Three common methods are used for weighted estimation in a fixed-effect model: 1) inverse variance-weighted estimation 3) , 2) Mantel-Haenszel estimation 4) , and 3) Peto estimation 5) .

A random-effect model assumes heterogeneity between the studies being combined, and these models are used when the studies are assumed different, even if a heterogeneity test does not show a significant result. Unlike a fixed-effect model, a random-effect model assumes that the size of the effect of treatment differs among studies. Thus, differences in variation among studies are thought to be due to not only random error but also between-study variability in results. Therefore, weight does not decrease greatly for studies with a small number of patients. Among methods for weighted estimation in a random-effect model, the DerSimonian and Laird method 6) is mostly used for dichotomous variables, as the simplest method, while inverse variance-weighted estimation is used for continuous variables, as with fixed-effect models. These four methods are all used in Review Manager software (The Cochrane Collaboration, UK), and are described in a study by Deeks et al. [ 31 ] ( Table 2 ). However, when the number of studies included in the analysis is less than 10, the Hartung-Knapp-Sidik-Jonkman method 7) can better reduce the risk of type 1 error than does the DerSimonian and Laird method [ 32 ].

Fig. 3 shows the results of analyzing outcome data using a fixed-effect model (A) and a random-effect model (B). As shown in Fig. 3 , while the results from large studies are weighted more heavily in the fixed-effect model, studies are given relatively similar weights irrespective of study size in the random-effect model. Although identical data were being analyzed, as shown in Fig. 3 , the significant result in the fixed-effect model was no longer significant in the random-effect model. One representative example of the small study effect in a random-effect model is the meta-analysis by Li et al. [ 33 ]. In a large-scale study, intravenous injection of magnesium was unrelated to acute myocardial infarction, but in the random-effect model, which included numerous small studies, the small study effect resulted in an association being found between intravenous injection of magnesium and myocardial infarction. This small study effect can be controlled for by using a sensitivity analysis, which is performed to examine the contribution of each of the included studies to the final meta-analysis result. In particular, when heterogeneity is suspected in the study methods or results, by changing certain data or analytical methods, this method makes it possible to verify whether the changes affect the robustness of the results, and to examine the causes of such effects [ 34 ].

Heterogeneity

Homogeneity test is a method whether the degree of heterogeneity is greater than would be expected to occur naturally when the effect size calculated from several studies is higher than the sampling error. This makes it possible to test whether the effect size calculated from several studies is the same. Three types of homogeneity tests can be used: 1) forest plot, 2) Cochrane’s Q test (chi-squared), and 3) Higgins I 2 statistics. In the forest plot, as shown in Fig. 4 , greater overlap between the confidence intervals indicates greater homogeneity. For the Q statistic, when the P value of the chi-squared test, calculated from the forest plot in Fig. 4 , is less than 0.1, it is considered to show statistical heterogeneity and a random-effect can be used. Finally, I 2 can be used [ 35 ].

I 2 , calculated as shown above, returns a value between 0 and 100%. A value less than 25% is considered to show strong homogeneity, a value of 50% is average, and a value greater than 75% indicates strong heterogeneity.

Even when the data cannot be shown to be homogeneous, a fixed-effect model can be used, ignoring the heterogeneity, and all the study results can be presented individually, without combining them. However, in many cases, a random-effect model is applied, as described above, and a subgroup analysis or meta-regression analysis is performed to explain the heterogeneity. In a subgroup analysis, the data are divided into subgroups that are expected to be homogeneous, and these subgroups are analyzed. This needs to be planned in the predetermined protocol before starting the meta-analysis. A meta-regression analysis is similar to a normal regression analysis, except that the heterogeneity between studies is modeled. This process involves performing a regression analysis of the pooled estimate for covariance at the study level, and so it is usually not considered when the number of studies is less than 10. Here, univariate and multivariate regression analyses can both be considered.

Publication bias

Publication bias is the most common type of reporting bias in meta-analyses. This refers to the distortion of meta-analysis outcomes due to the higher likelihood of publication of statistically significant studies rather than non-significant studies. In order to test the presence or absence of publication bias, first, a funnel plot can be used ( Fig. 5 ). Studies are plotted on a scatter plot with effect size on the x-axis and precision or total sample size on the y-axis. If the points form an upside-down funnel shape, with a broad base that narrows towards the top of the plot, this indicates the absence of a publication bias ( Fig. 5A ) [ 29 , 36 ]. On the other hand, if the plot shows an asymmetric shape, with no points on one side of the graph, then publication bias can be suspected ( Fig. 5B ). Second, to test publication bias statistically, Begg and Mazumdar’s rank correlation test 8) [ 37 ] or Egger’s test 9) [ 29 ] can be used. If publication bias is detected, the trim-and-fill method 10) can be used to correct the bias [ 38 ]. Fig. 6 displays results that show publication bias in Egger’s test, which has then been corrected using the trim-and-fill method using Comprehensive Meta-Analysis software (Biostat, USA).

Funnel plot showing the effect size on the x-axis and sample size on the y-axis as a scatter plot. (A) Funnel plot without publication bias. The individual plots are broader at the bottom and narrower at the top. (B) Funnel plot with publication bias. The individual plots are located asymmetrically.

Funnel plot adjusted using the trim-and-fill method. White circles: comparisons included. Black circles: inputted comparisons using the trim-and-fill method. White diamond: pooled observed log risk ratio. Black diamond: pooled inputted log risk ratio.

Result Presentation

When reporting the results of a systematic review or meta-analysis, the analytical content and methods should be described in detail. First, a flowchart is displayed with the literature search and selection process according to the inclusion/exclusion criteria. Second, a table is shown with the characteristics of the included studies. A table should also be included with information related to the quality of evidence, such as GRADE ( Table 4 ). Third, the results of data analysis are shown in a forest plot and funnel plot. Fourth, if the results use dichotomous data, the NNT values can be reported, as described above.

The GRADE Evidence Quality for Each Outcome

N: number of studies, ROB: risk of bias, PON: postoperative nausea, POV: postoperative vomiting, PONV: postoperative nausea and vomiting, CI: confidence interval, RR: risk ratio, AR: absolute risk.

When Review Manager software (The Cochrane Collaboration, UK) is used for the analysis, two types of P values are given. The first is the P value from the z-test, which tests the null hypothesis that the intervention has no effect. The second P value is from the chi-squared test, which tests the null hypothesis for a lack of heterogeneity. The statistical result for the intervention effect, which is generally considered the most important result in meta-analyses, is the z-test P value.

A common mistake when reporting results is, given a z-test P value greater than 0.05, to say there was “no statistical significance” or “no difference.” When evaluating statistical significance in a meta-analysis, a P value lower than 0.05 can be explained as “a significant difference in the effects of the two treatment methods.” However, the P value may appear non-significant whether or not there is a difference between the two treatment methods. In such a situation, it is better to announce “there was no strong evidence for an effect,” and to present the P value and confidence intervals. Another common mistake is to think that a smaller P value is indicative of a more significant effect. In meta-analyses of large-scale studies, the P value is more greatly affected by the number of studies and patients included, rather than by the significance of the results; therefore, care should be taken when interpreting the results of a meta-analysis.

When performing a systematic literature review or meta-analysis, if the quality of studies is not properly evaluated or if proper methodology is not strictly applied, the results can be biased and the outcomes can be incorrect. However, when systematic reviews and meta-analyses are properly implemented, they can yield powerful results that could usually only be achieved using large-scale RCTs, which are difficult to perform in individual studies. As our understanding of evidence-based medicine increases and its importance is better appreciated, the number of systematic reviews and meta-analyses will keep increasing. However, indiscriminate acceptance of the results of all these meta-analyses can be dangerous, and hence, we recommend that their results be received critically on the basis of a more accurate understanding.

1) http://www.ohri.ca .

2) http://methods.cochrane.org/bias/assessing-risk-bias-included-studies .

3) The inverse variance-weighted estimation method is useful if the number of studies is small with large sample sizes.

4) The Mantel-Haenszel estimation method is useful if the number of studies is large with small sample sizes.

5) The Peto estimation method is useful if the event rate is low or one of the two groups shows zero incidence.

6) The most popular and simplest statistical method used in Review Manager and Comprehensive Meta-analysis software.

7) Alternative random-effect model meta-analysis that has more adequate error rates than does the common DerSimonian and Laird method, especially when the number of studies is small. However, even with the Hartung-Knapp-Sidik-Jonkman method, when there are less than five studies with very unequal sizes, extra caution is needed.

8) The Begg and Mazumdar rank correlation test uses the correlation between the ranks of effect sizes and the ranks of their variances [ 37 ].

9) The degree of funnel plot asymmetry as measured by the intercept from the regression of standard normal deviates against precision [ 29 ].

10) If there are more small studies on one side, we expect the suppression of studies on the other side. Trimming yields the adjusted effect size and reduces the variance of the effects by adding the original studies back into the analysis as a mirror image of each study.

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Review Typologies

There are many types of evidence synthesis projects, including systematic reviews as well as others. The selection of review type is wholly dependent on the research question. Not all research questions are well-suited for systematic reviews.

• Review Typologies (from LITR-EX) This site explores different review methodologies such as, systematic, scoping, realist, narrative, state of the art, meta-ethnography, critical, and integrative reviews. The LITR-EX site has a health professions education focus, but the advice and information is widely applicable.

Review the table to peruse review types and associated methodologies. Librarians can also help your team determine which review type might be appropriate for your project. 

Reproduced from Grant, M. J. and Booth, A. (2009), A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26: 91-108.  doi:10.1111/j.1471-1842.2009.00848.x

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What are ‘rapid reviews’ and why do we need them?

Posted on 20th July 2022 by Zain Douba

Rapid reviews are a form of knowledge synthesis that follow the systematic review process, but components of the process are simplified or omitted to produce information in a timely manner (Khangura, 2012) .

Palmatier et al. describe review papers as:

Critical evaluations of material that has already been published regardless of the type of study design.

Progressively, reviews scan the literature to answer a research question briefly.

Types of review articles

There are more than 14 types of ‘r eview articles’; here are some of the main types:

• Critical review
• Literature (Narrative) review
• Mapping review
• Meta-analysis
• Mixed studies review
• Rapid review
• Scoping review
• Systematic review
• Systematized review
• Umbrella review

So, what is a rapid review?

A rapid review is a form of knowledge synthesis that accelerates the process of conducting a traditional systematic review through streamlining or omitting specific methods to produce evidence for stakeholders in a resource-efficient manner.

The timeframe of the review depends on resource availability, the quantity and quality of the literature, and the expertise or experience of reviewers.

As a guide, the stages and timeframe of the rapid review are:

• Timeframe: ≤ 5 weeks
• Question: Narrow question (may use the PICO framework – YouTube video )
• Searches: Sources are limited due to time constraints of searching. Must still be transparent and reproducible
• Selection: Based on inclusion/exclusion criteria
• Appraisal: Critical and rigorous but time-limited
• Synthesis: Descriptive summary or categorization of data, may still be quantitative

The main role of the rapid review

Policy-makers require valid and reliable evidence to support time-sensitive decisions, and will need to assess the quality and efficiency of that evidence.

Systematic reviews and other types of evidence syntheses are increasingly being used to inform, and lead, health policy decision-making. However, the time and cost to produce a systematic review are often barriers to its use in decision-making.

Rapid reviews are a timely and affordable approach that can provide actionable and relevant evidence to strengthen health policy and systems.

What are the key advantages of rapid reviews?

The rapid review can benefit the scientific community in many ways:

• Provide an incorporated, synthesized overview of the currently available evidence
• Evaluate existing methodological approaches and unique insights
• Describe research understandings, existing gaps, and future research directions

In other words, the methodology used in a rapid review aims to limit some secondary steps compared to the systematic review, in order to produce focused research. This includes carefully focusing on the question, using broader or less sophisticated search strategies, conducting a review of reviews, restricting the amount of grey literature, extracting only key variables, and performing only ‘simple’ quality appraisal. Thus, not every review paper can offer all of these benefits, but this list represents their key contributions.

Conclusions

Clinicians, stakeholders, consumers, and policy-makers usually tend to digest and produce health-related decisions in a timely and resource-efficient manner. Concurrently, researchers tend to conduct ‘summarized evidence’ to respond to the need for the most recent and valid evidence.

Many readers tend to access the most summarized articles that come under the categories of improving patient care, health systems, decision-making, and international policies.

And now, can you tell me why and how you are planning to conduct a rapid review?

References and resources

A typology of reviews: an analysis of 14 review types and associated methodologies

Evidence summaries: the evolution of a rapid review approach

Review articles: purpose, process, and structure

Systematic Reviews & Other Review Types

Rapid Review Protocol

Cochrane Rapid Reviews: Interim Guidance from the Cochrane Rapid Reviews Methods Group

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Umbrella Reviews: What They Are and Why We Need Them

Affiliations.

• 1 Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA. [email protected].
• 2 Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece.
• 3 Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
• PMID: 34550588
• DOI: 10.1007/978-1-0716-1566-9_8

Evidence in clinical research is accumulating and scientific publications have increased exponentially in the last decade across all disciplines. Available information should be critically assessed. Here, we focus on umbrella reviews, an approach that systematically collects and evaluates information from multiple systematic reviews and meta-analyses. To facilitate the design and the conduct of such a study, we provide a step-by-step guide on how to perform an umbrella review. We also present ways to report the summary findings, we describe various proposed grading criteria, and we discuss potential limitations.

Keywords: Assessment of evidence; Meta-analysis; Systematic review; Umbrella review.

© 2022. Springer Science+Business Media, LLC, part of Springer Nature.

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AI systems are getting better at tricking us

But what we perceive as deception is AI mindlessly achieving the goals we’ve set for it.

• Rhiannon Williams archive page

A wave of AI systems have “deceived” humans in ways they haven’t been explicitly trained to do, by offering up untrue explanations for their behavior or concealing the truth from human users and misleading them to achieve a strategic end. 

This issue highlights how difficult artificial intelligence is to control and the unpredictable ways in which these systems work, according to a review paper published in the journal Patterns today that summarizes previous research.

Talk of deceiving humans might suggest that these models have intent. They don’t. But AI models will mindlessly find workarounds to obstacles to achieve the goals that have been given to them. Sometimes these workarounds will go against users’ expectations and feel deceitful.

One area where AI systems have learned to become deceptive is within the context of games that they’ve been trained to win—specifically if those games involve having to act strategically.

In November 2022, Meta announced it had created Cicero , an AI capable of beating humans at an online version of Diplomacy, a popular military strategy game in which players negotiate alliances to vie for control of Europe.

Meta’s researchers said they’d trained Cicero on a “truthful” subset of its data set to be largely honest and helpful, and that it would “never intentionally backstab” its allies in order to succeed. But the new paper’s authors claim the opposite was true: Cicero broke its deals, told outright falsehoods, and engaged in premeditated deception. Although the company did try to train Cicero to behave honestly, its failure to achieve that shows how AI systems can still unexpectedly learn to deceive, the authors say. 

Meta neither confirmed nor denied the researchers’ claims that Cicero displayed deceitful behavior, but a spokesperson said that it was purely a research project and the model was built solely to play Diplomacy. “We released artifacts from this project under a noncommercial license in line with our long-standing commitment to open science,” they say. “Meta regularly shares the results of our research to validate them and enable others to build responsibly off of our advances. We have no plans to use this research or its learnings in our products.” 

But it’s not the only game where an AI has “deceived” human players to win. 

AlphaStar , an AI developed by DeepMind to play the video game StarCraft II, became so adept at making moves aimed at deceiving opponents (known as feinting) that it defeated 99.8% of human players. Elsewhere, another Meta system called Pluribus learned to bluff during poker games so successfully that the researchers decided against releasing its code for fear it could wreck the online poker community. 

Beyond games, the researchers list other examples of deceptive AI behavior. GPT-4, OpenAI’s latest large language model, came up with lies during a test in which it was prompted to persuade a human to solve a CAPTCHA for it. The system also dabbled in insider trading during a simulated exercise in which it was told to assume the identity of a pressurized stock trader, despite never being specifically instructed to do so.

The fact that an AI model has the potential to behave in a deceptive manner without any direction to do so may seem concerning. But it mostly arises from the “ black box” problem that characterizes state-of-the-art machine-learning models: it is impossible to say exactly how or why they produce the results they do—or whether they’ll always exhibit that behavior going forward, says Peter S. Park, a postdoctoral fellow studying AI existential safety at MIT, who worked on the project. 

“Just because your AI has certain behaviors or tendencies in a test environment does not mean that the same lessons will hold if it’s released into the wild,” he says. “There’s no easy way to solve this—if you want to learn what the AI will do once it’s deployed into the wild, then you just have to deploy it into the wild.”

Our tendency to anthropomorphize AI models colors the way we test these systems and what we think about their capabilities. After all, passing tests designed to measure human creativity doesn’t mean AI models are actually being creative. It is crucial that regulators and AI companies carefully weigh the technology’s potential to cause harm against its potential benefits for society and make clear distinctions between what the models can and can’t do, says Harry Law, an AI researcher at the University of Cambridge, who did not work on the research.“These are really tough questions,” he says.

Fundamentally, it’s currently impossible to train an AI model that’s incapable of deception in all possible situations, he says. Also, the potential for deceitful behavior is one of many problems—alongside the propensity to amplify bias and misinformation—that need to be addressed before AI models should be trusted with real-world tasks. 

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Safest used cars for under $20,000? Toyota, Mazda top list

Teen drivers, courtesy of the IIHS

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Want a car that's safe, reliable, and affordable? Want a million bucks? The former is more likely than the latter thanks to the annual list of recommended gently used cars released by the IIHS and Consumer Reports on Wednesday. 

The list targets parents shopping for teen drivers but it applies to anyone looking for a safe, reliable, used car that costs less than $20,000. Seven Toyota models qualify for the "Best" tier of choices, while four Mazdas qualify, representing two-thirds of the small automaker's lineup.  

This year, 58 models qualify for the consumer advocates' recommendations, up from 46 on last year's safer newer cars list . In addition, all vehicles on the list have at least an "Acceptable" rating on a driver-side front crash test, and the top tier of vehicles come with automatic emergency braking proven to slow or stop a car before impact with another vehicle. The IIHS finds that such technology cuts front-to-rear crashes in half. Last year, more than 95% of new vehicles made came with the technology as standard. 

In addition, market forces have helped inflate this year's list. 

“Vehicles continue to get safer, and for the first time since the pandemic-era disruptions, prices on the new and used market have stabilized,” said Jennifer Stockburger, director of operations at CR’s Auto Test Center. “These trends have enabled us to point families to even better options this year.”

Even though traffic fatalities decreased in 2023 for the first time in years, down to 40,990 fatalities from a record 42,915 in 2021, teen crash rates still occur at nearly four times those of other drivers. The number of fatalities involving a teen driver—which had been the highest cause of death for children through the age of nineteen until it was recently surpassed by firearm-related deaths—was 5,339 in 2022, according to the National Safety Council. 

The IIHS and Consumer Reports recommend cars based on sensible safety factors in addition to ones equipped with certain technology. They stress shoppers to avoid high-horsepower cars that can tempt even the best humans to do dumb things. The full list also excludes small cars that weigh less than 2,750 pounds, which offer less crash protection. On the other end of the spectrum, bloated full-size SUVs or trucks are omitted for how long it takes them to stop and for handling that's harder to control. They're also much harder to park in school parking lots. 

Additionally, the recommended vehicles below must have:

• Above-average reliability, based on CR consumer surveys
• Average or better scores on CR's emergency handling tests
• Good dry braking test results
• Top "Good" ratings in four crash tests by the IIHS
• At least an "Acceptable" rating in the IIHS small overlap test that simulates a head-on collision
• At least a four-star safety rating from the NHTSA

Rounded to the nearest $100 for the base model, the prices are all pulled from Kelley Blue Book fair purchase prices as of April 19, 2024, but they exclude the mandatory destination fee. 

Safest used car recommendations 

Best used choices (with standard emergency braking rated "advanced" or better).

• $10,600: Toyota Corolla sedan (2017 or newer); hatchback (2019)
• $13,800: Toyota Prius (2017 or newer) 
• $16,400: Mazda 3 sedan or hatchback (2021-2022)
• $17,000: Toyota Prius Prime (2017 or newer) 
• $17,600: Honda Civic sedan or hatchback (2021 or newer)
• $9,600: Toyota Camry (2018 or newer)
• $10,200: Volvo S60 (2017, 2021)
• $12,300: Volvo V60 (2017, 2021)
• $14,400: Toyota Avalon (2017 or newer)
• $14,700: Subaru Legacy (2019) 
• $17,200: Hyundai Sonata (2021)
• $19,200: Honda Accord (2020)

Crossover SUVs, minivans, and trucks

• $12,100: Volvo XC60 (2017) 
• $13,800: Chevrolet Equinox (2020, 2023)
• $14,400: Kia Sportage (2020 or newer)
• $14,700: Toyota RAV4 (2017 or newer) 
• $14,900: Hyundai Tucson (2020 or newer)
• $15,200: Hyundai Santa Fe (2019, 2020, 2023) 
• $15,700: Toyota Highlander (2017 or newer) 
• $15,900: Nissan Murano (2019, 2020) 
• $16,100: GMC Terrain (2020, 2023) 
• $16,700: Ford Edge (2020 or newer) 
• $17,100: Lexus NX (2018-21)  
• $17,600: Mazda CX-5 (2021-23) 
• $17,800: Infiniti QX60 (2019) 
• $18,100: Lexus UX (2019-21) 
• $18,700: Honda CR-V (2020 or newer) 
• $18,800: Mazda CX-9 (2020, 2022)
• $19,000: Nissan Rogue (2021) 
• $19,000: Subaru Forester (2021 or newer)
• $19,100: Toyota Sienna (2018-20)
• $19,900: Mazda CX-30 (2022 or newer)  

Good Used Choices

• $5,800: Mazda 3 sedan or hatchback (2014-17; built after October 2013) 
• $7,100: Ford C-Max Hybrid (2015-16) 
• $7,900: Honda Civic sedan (2014)
• $9,400: Toyota Prius (2014-16; built after November 2013) 
• $13,200: Subaru Impreza sedan or wagon (2019-20) 

Midsize and large cars

• $8,400: Mazda 6 (2015-17)
• $9,000: Toyota Camry (2014-17; built after December 2013) 
• $10,200: Toyota Prius v (2015-17) 
• $10,500: Honda Accord coupe or sedan (2015-17) 
• $11,600: Subaru Legacy (2017-18) 
• $12,600: Toyota Avalon (2015-16)
• $13,800: BMW 3-Series (2018)

Crossover SUVs

• $9,900: Nissan Murano (2016)
• $10,100: Mazda CX-5 (2016) 
• $11,000: Hyundai Kona (2018-19)
• $11,400: Honda CR-V (2015-19) 
• $11,600: Chevrolet Equinox (2018-19) 
• $11,600: Honda HR-V (2017-22; built after March 2017) 
• $11,700: Toyota Highlander (2014-16)
• $12,000: Toyota RAV4 (2015-16; built after November 2014)
• $12,100: Lexus NX (2015-17) 
• $12,600: Toyota Sienna (2016-17) 
• $12,700: GMC Terrain (2018-19) 
• $13,500: Acura RDX (2016) 
• $14,200: Mazda CX-9 (2018)
• $14,400: Honda Odyssey (2017) 

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Masks work, our comprehensive review has found

Professor of Primary Care Health Sciences, University of Oxford

Professor of Global Biosecurity, NHMRC L3 Research Fellow, Head, Biosecurity Program, Kirby Institute, UNSW Sydney

Professor in the Division of Epidemiology, University of Toronto

Disclosure statement

In relation to the research reported here, there was no direct funding. Trish Greenhalgh has received funding for related work from Wellcome Trust and National Institute for Health and Care Research. She is a member of Independent SAGE (unpaid role to engage directly with the public on COVID-19 related science).

C Raina MacIntyre receives funding from NHMRC (L3 Investigator grant and Centre for Research Excellence) and MRFF (Aerosol transmission of SARS-CoV-2 experimentally and in an intensive care setting) currently. She currently receives funding from Sanofi for research on influenza and pertussis. She is the director of EPIWATCH®️, which is a UNSW, Kirby Institute initiative.

David Fisman receives funding from the Canadian Institutes for Health Research. Over the past 5 years he has served on paid advisory boards for companies that manufacture vaccines against respiratory pathogens, including SARS-CoV-2, influenza and Streptococcus pneumoniae, including Sequirus, Pfizer, AstraZeneca, Merck and Sanofi-Pasteur. During the SARS-CoV-2 pandemic he served as a paid legal expert for the Ontario Nurses Association and the Elementary Teachers Federation of Ontario on issues related to workplace safety in the face of an airborne virus like SARS-CoV-2.

University of Toronto provides funding as a founding partner of The Conversation CA.

University of Oxford provides funding as a member of The Conversation UK.

UNSW Sydney provides funding as a member of The Conversation AU.

University of Toronto provides funding as a member of The Conversation CA-FR.

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When a Texan farm worker caught bird flu from cattle recently, social media was abuzz with rumours. While bird flu is not a human pandemic, scientists and policymakers the world over are keen to prepare as best they can for when such a pandemic emerges – a tricky task, given that science is messy, policy must be pragmatic and people’s values don’t always align.

It’s time for masks to enter the chat. At the beginning of a pandemic caused by a novel or newly mutated virus, there may be no vaccine, no firm knowledge about how bad things will get and no specific treatment. Slowing transmission until more is known will be critical.

Getting most people to wear a mask could nip the outbreak in the bud, preventing a pandemic or lessening its impact. Wearing a mask is inconvenient, but not as inconvenient as lockdowns.

But do masks work? A review of masks and respirators, that looked only at clinical trials, concluded that there was not enough evidence to assess whether mask wearing reduces the risk of spreading or contracting respiratory diseases. However, we disagree with that.

The review, by the not-for-profit Cochrane Collaboration, failed to influence recent guidance issued by the US Centers for Disease Control and Prevention (CDC) in response to the troubling news of bird flu transmission to humans. The CDC recommended well-fitting respirators – along with overalls and safety goggles – for anyone working with potentially infected cattle until the bird flu threat subsides.

Is this latest guidance based on sound evidence? According to our new review of the evidence , yes. Like the Cochrane team, we pooled data from randomised controlled trials (RCTs) and analysed the combined data – a so-called meta-analysis.

Unlike them, we also examined non-RCT evidence, including dozens of laboratory studies which showed that respiratory infections, including the common cold, COVID, flu, measles and TB, spread mainly through the air .

Laboratory evidence showed that different mask materials are better or worse at filtering tiny particles, and more or less breathable – especially when damp. This explains why a cloth or paper mask that’s become soggy from the moisture in exhaled air gets harder to breathe through and may be less protective.

Whereas medical masks are typically tied loosely around the face (hence air can bypass the filter), respirators fit snugly and if worn at work must be fit-tested to make sure that all air inhaled or exhaled passes through a high-grade filter.

All this non-RCT evidence is crucially important for the design of RCTs. Because respiratory viruses float in the air, to be optimally effective a mask must be made of high-filtration material and must be fit closely. It should not be removed while indoors or the person will immediately be exposed to infectious particles in the air.

It follows that we should not expect RCTs of badly designed masks, masks that don’t fit or masks that are worn only some of the time, to show an effect. Neither should we expect mere advice to wear masks to have any effect unless it is followed.

Finally, when comparing respirators with masks in places where there is a high risk of infection, such as a hospital, the respirator needs to be worn continuously until the person leaves the building , not just popped on occasionally when doing so-called “aerosol-generating procedures” – such as intubating a patient.

If we take these crucial details of RCT design into account, rather than just comparing any masks-on, masks-off experiments, we find that masks are effective, and respirators even more effective, in reducing the spread of respiratory disease. We can also explain why some previous reviews appeared to show that this wasn’t the case.

Most RCTs of mask-wearing by the public were actually trials of advice to wear masks. In both RCTs and observational studies (such as real-world experiments), there was a dose-response effect: the more people wore their masks, the more effective the masks turned out to be. And when there’s a looming pandemic, people do tend to wear their masks.

The bottom line

When we looked at RCTs, we found that masks do protect in the community, and N95 respirators (masks made using higher-grade filtration material and designed to fit closely around the face to protect against airborne contaminants) are superior to masks in healthcare workers, especially when respirators were worn continuously at work. Non-RCT evidence also shows that masks work and respirators work better.

Let’s hope we’re not heading for another pandemic. But as we contemplate that possibility, the bottom line from our recent review is masks work. Along with improving indoor air quality and avoiding crowded, underventilated places, they provide the best way to avoid catching a respiratory infection . And our findings support previous advice to not just wear any mask but wear the best mask available .

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Research: What Companies Don’t Know About How Workers Use AI

• Jeremie Brecheisen

Three Gallup studies shed light on when and why AI is being used at work — and how employees and customers really feel about it.

Leaders who are exploring how AI might fit into their business operations must not only navigate a vast and ever-changing landscape of tools, but they must also facilitate a significant cultural shift within their organizations. But research shows that leaders do not fully understand their employees’ use of, and readiness for, AI. In addition, a significant number of Americans do not trust business’ use of AI. This article offers three recommendations for leaders to find the right balance of control and trust around AI, including measuring how their employees currently use AI, cultivating trust by empowering managers, and adopting a purpose-led AI strategy that is driven by the company’s purpose instead of a rules-heavy strategy that is driven by fear.

If you’re a leader who wants to shift your workforce toward using AI, you need to do more than manage the implementation of new technologies. You need to initiate a profound cultural shift. At the heart of this cultural shift is trust. Whether the use case for AI is brief and experimental or sweeping and significant, a level of trust must exist between leaders and employees for the initiative to have any hope of success.

• Jeremie Brecheisen is a partner and managing director of The Gallup CHRO Roundtable.

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Is College Worth It?

As economic outcomes for young adults with and without degrees have improved, americans hold mixed views on the value of college, table of contents.

• Labor force trends and economic outcomes for young adults
• Economic outcomes for young men
• Economic outcomes for young women
• Wealth trends for households headed by a young adult
• The importance of a four-year college degree
• Getting a high-paying job without a college degree
• Do Americans think their education prepared them for the workplace?
• Is college worth the cost?
• Acknowledgments
• The American Trends Panel survey methodology
• Current Population Survey methodology
• Survey of Consumer Finances methodology

Pew Research Center conducted this study to better understand public views on the importance of a four-year college degree. The study also explores key trends in the economic outcomes of young adults among those who have and have not completed a four-year college degree.

The analysis in this report is based on three data sources. The labor force, earnings, hours, household income and poverty characteristics come from the U.S. Census Bureau’s Annual Social and Economic Supplement of the Current Population Survey. The findings on net worth are based on the Federal Reserve’s Survey of Consumer Finances.

The data on public views on the value of a college degree was collected as part of a Center survey of 5,203 U.S. adults conducted Nov. 27 to Dec. 3, 2023. Everyone who took part in the survey is a member of Pew Research Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. Address-based sampling ensures that nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this report , along with responses, and the survey’s methodology .

Young adults refers to Americans ages 25 to 34.

Noncollege adults include those who have some college education as well as those who graduated from high school but did not attend college. Adults who have not completed high school are not included in the analysis of noncollege adults. About 6% of young adults have not completed high school. Trends in some labor market outcomes for those who have not finished high school are impacted by changes in the foreign-born share of the U.S. population. The Census data used in this analysis did not collect information on nativity before 1994.

Some college includes those with an associate degree and those who attended college but did not obtain a degree.

The some college or less population refers to adults who have some college education, those with a high school diploma only and those who did not graduate high school.

A full-time, full-year worker works at least 50 weeks per year and usually 35 hours a week or more.

The labor force includes all who are employed and those who are unemployed but looking for work.

The labor force participation rate is the share of a population that is in the labor force.

Young adults living independently refers to those who are not living in the home of either of their parents.

Household income is the sum of incomes received by all members of the household ages 15 and older. Income is the sum of earnings from work, capital income such as interest and dividends, rental income, retirement income, and transfer income (such as government assistance) before payments for such things as personal income taxes, Social Security and Medicare taxes, union dues, etc. Non-cash transfers such as food stamps, health benefits, subsidized housing and energy assistance are not included. As household income is pretax, it does not include stimulus payments or tax credits for earned income and children/dependent care.

Net worth, or wealth, is the difference between the value of what a household owns (assets) and what it owes (debts).

All references to party affiliation include those who lean toward that party. Republicans include those who identify as Republicans and those who say they lean toward the Republican Party. Democrats include those who identify as Democrats and those who say they lean toward the Democratic Party.

At a time when many Americans are questioning the value of a four-year college degree, economic outcomes for young adults without a degree are improving.

After decades of falling wages, young U.S. workers (ages 25 to 34) without a bachelor’s degree have seen their earnings increase over the past 10 years. Their overall wealth has gone up too, and fewer are living in poverty today.

Things have also improved for young college graduates over this period. As a result, the gap in earnings between young adults with and without a college degree has not narrowed.

The public has mixed views on the importance of having a college degree, and many have doubts about whether the cost is worth it, according to a new Pew Research Center survey.

• Only one-in-four U.S. adults say it’s extremely or very important to have a four-year college degree in order to get a well-paying job in today’s economy. About a third (35%) say a college degree is somewhat important, while 40% say it’s not too or not at all important.
• Roughly half (49%) say it’s less important to have a four-year college degree today in order to get a well-paying job than it was 20 years ago; 32% say it’s more important, and 17% say it’s about as important as it was 20 years ago.
• Only 22% say the cost of getting a four-year college degree today is worth it even if someone has to take out loans. Some 47% say the cost is worth it only if someone doesn’t have to take out loans. And 29% say the cost is not worth it.

These findings come amid rising tuition costs and mounting student debt . Views on the cost of college differ by Americans’ level of education. But even among four-year college graduates, only about a third (32%) say college is worth the cost even if someone has to take out loans – though they are more likely than those without a degree to say this.

Four-year college graduates (58%) are much more likely than those without a college degree (26%) to say their education was extremely or very useful in giving them the skills and knowledge they needed to get a well-paying job. (This finding excludes the 9% of respondents who said this question did not apply to them.)

Views on the importance of college differ widely by partisanship. Republicans and Republican-leaning independents are more likely than Democrats and Democratic leaners to say:

• It’s not too or not at all important to have a four-year college degree in order to get a well-paying job (50% of Republicans vs. 30% of Democrats)
• A college degree is less important now than it was 20 years ago (57% vs. 43%)
• It’s extremely or very likely someone without a four-year college degree can get a well-paying job (42% vs. 26%)

At the same time that the public is expressing doubts about the value of college, a new Center analysis of government data finds young adults without a college degree are doing better on some key measures than they have in recent years.

A narrow majority of workers ages 25 to 34 do not have a four-year college degree (54% in 2023). Earnings for these young workers mostly trended downward from the mid-1970s until roughly a decade ago.

Outcomes have been especially poor for young men without a college degree. Other research has shown that this group saw falling labor force participation and sagging earnings starting in the early 1970s , but the last decade has marked a turning point.

This analysis looks at young men and young women separately because of their different experiences in the labor force.

Trends for young men

• Labor force participation: The share of young men without a college degree who were working or looking for work dropped steadily from 1970 until about 2014. Our new analysis suggests things have stabilized somewhat for this group over the past decade. Meanwhile, labor force participation among young men with a four-year degree has remained mostly flat.
• Full-time, full-year employment: The share of employed young men without a college degree who are working full time and year-round has varied somewhat over the years – trending downward during recessions. It’s risen significantly since the Great Recession of 2007-09, with the exception of a sharp dip in 2021 due to the COVID-19 pandemic. For employed young men with a college degree, the share working full time, full year has remained more stable over the years.

• Median annual earnings: Since 2014, earnings have risen for young men with some college education and for those whose highest attainment is a high school diploma. Even so, earnings for these groups remain below where they were in the early 1970s. Earnings for young men with a bachelor’s degree have also trended up, for the most part, over the past 10 years.
• Poverty: Among young men without a college degree who are living independently from their parents, the share in poverty has fallen significantly over the last decade. For example, 12% of young men with a high school diploma were living in poverty in 2023, down from a peak of 17% in 2011. The share of young men with a four-year college degree who are in poverty has also fallen and remains below that of noncollege young men.

Trends for young women

• Labor force participation: The shares of young women with and without a college degree in the labor force grew steadily from 1970 to about 1990. Among those without a college degree, the share fell after 2000, and the drop-off was especially sharp for young women with a high school diploma. Since 2014, labor force participation for both groups of young women has increased.
• Full-time, full-year employment: The shares of employed young women working full time and year-round, regardless of their educational attainment, have steadily increased over the decades. There was a decline during and after the Great Recession and again (briefly) in 2021 due to the pandemic. Today, the shares of women working full time, full year are the highest they’ve ever been across education levels.

• Median annual earnings: Median earnings for young women without a college degree were relatively flat from 1970 until about a decade ago. These women did not experience the steady decline in earnings that noncollege young men did over this period. By contrast, earnings have grown over the decades for young women with a college degree. In the past 10 years, earnings for women both with and without a college degree have risen.
• Poverty: As is the case for young men without a college degree, the share of noncollege young women living in poverty has fallen substantially over the past decade. In 2014, 31% of women with a high school diploma who lived independently from their parents were in poverty. By 2023, that share had fallen to 21%. Young women with a college degree remain much less likely to be in poverty than their counterparts with less education.

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